Traditionally, a durability validation process for powertrain system and components has three different levels. These levels include vehicle level testing, system level testing, and component level testing. In vehicle level testing, vehicles are commonly tested at a proving ground based on specific powertrain endurance schedules. For system level testing, offline system data such as torque-at-speed and gears are utilized to create transmission and differential dynamometer testing schedules. For component level testing, offline component data such as rainflow cycle results are utilized to develop component bench test criteria.
In practice, a vehicle durability design and validation target is based on a proving ground powertrain endurance schedule. The proving ground powertrain endurance schedule is derived based on percentile customer usage information. For example, a proving ground powertrain endurance schedule can be based on information reflecting the 95th percentile of customer usage. Specifically, the proving ground powertrain schedule is derived by studying the poll data from a pool of vehicle owners who volunteer to participate in answering survey questionnaires. The answers are then translated into engineering requirements as part of the proving ground schedule. However, the resultant engineering requirements tend to be highly subjective due to the subjective nature of customer interpretation and responses to the survey questionnaires.
Additional variableness occurs at the powertrain system and component level testing. Traditionally, for powertrain system and component level testing, the design and validation test target have included historical data collected from various sources and data acquired from a test vehicle running on a proving ground based on the above-mentioned proving ground schedules. However, historical data often lacks context and may not be able to be correlated with actual use patterns. Further, the acquired proving ground data tends to be subjective and prone to error. As a result, the use of the data is limited due to a lack of confidence in the data accuracy. When the data is used, design targets tend to be set artificially high in order to compensate for the low confidence rating.
Thus, there is a need to acquire real-time customer usage data to substantiate and define a more accurate representative of 95th percentile customer usage in an objective manner. There is also a need to improve the quality of data used to construct vehicle simulations and models for prototype durability testing. Additionally, there is a need to improve the efficiency of obtaining and processing component data so as to improve the design and testing stages of powertrain development.